code: def linear_columns(include_label_column=False):
      """Return feature_columns for linear model."""
      language = tf.feature_column.indicator_column(
          tf.feature_column.categorical_column_with_vocabulary_list(
              key="language", vocabulary_list=("english", "chinese")
          )
      )
      age = tf.feature_column.numeric_column(
          key="age", default_value=0.0
      )
      features = [age, language]
      if include_label_column:
          label = tf.feature_column.numeric_column(
              key="label", default_value=0.0
          )
          features.append(label)
      return features
  
file_path: model-analysis/tensorflow_model_analysis/eval_saved_model/example_trainers/util.py
imports: import tensorflow
  from tensorflow_model_analysis.eval_saved_model import export
  from tensorflow_model_analysis.eval_saved_model import util
label_desc: Use image node_id 4 for calling a global function with example usage: linear_columns(include_label_column) and returns: features